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Latest from AI Agent Research Hub

32 recent articles
AI Agent Research Hub
AI Agent Research Hub
Jul 19, 2026 · Artificial Intelligence

Efficient Transonic Wing Flow Simulation and Shock Capture Using Euler‑PINN

This tutorial walks through building and training a physics‑informed neural network (Euler‑PINN) in JAX to simulate transonic airfoil flow, detailing the governing Euler equations, structured‑grid coordinate mapping, loss formulation, L‑BFGS optimization, and a thorough comparison with a high‑resolution finite‑volume reference solution that highlights both accuracy and efficiency trade‑offs.

Computational fluid dynamicsEuler-PINNJAX
0 likes · 20 min read
Efficient Transonic Wing Flow Simulation and Shock Capture Using Euler‑PINN
AI Agent Research Hub
AI Agent Research Hub
Jul 16, 2026 · Artificial Intelligence

Solving the Falkner‑Skan Viscous Boundary Layer with Physics‑Informed Neural Networks (PINN)

This tutorial demonstrates how to solve the two‑dimensional steady incompressible Falkner‑Skan viscous boundary‑layer equations using a physics‑informed neural network implemented in JAX, detailing the network architecture, loss construction, two‑stage training strategy, and achieving sub‑0.01 % relative errors with only 1 % interior collocation points.

Falkner‑SkanJAXPINN
0 likes · 17 min read
Solving the Falkner‑Skan Viscous Boundary Layer with Physics‑Informed Neural Networks (PINN)
AI Agent Research Hub
AI Agent Research Hub
Jun 19, 2026 · Artificial Intelligence

DeepONet Neural Operator for Fast Prediction of Non‑Smooth Discontinuities in the Sod Shock Tube

This tutorial presents a complete DeepONet workflow—two‑step separated training, Rowdy activation, SVD orthogonalisation, and a 10‑member ensemble—that predicts the density, velocity and pressure fields of the one‑dimensional Sod shock‑tube problem with an average test‑set relative error of 2.23% after only 22 minutes of training on an RTX 4090.

DeepONetEnsembleJAX
0 likes · 22 min read
DeepONet Neural Operator for Fast Prediction of Non‑Smooth Discontinuities in the Sod Shock Tube
AI Agent Research Hub
AI Agent Research Hub
May 19, 2026 · Artificial Intelligence

Master Data‑Driven Neural Operators: DeepONet, POD‑DeepONet, FNO‑2D and Time‑Stepping FNO‑1D in One Tutorial

This tutorial presents a comprehensive JAX implementation and analysis of four neural‑operator methods—standard DeepONet, POD‑DeepONet, 2‑D Fourier Neural Operator, and time‑stepping 1‑D FNO—applied to the 1‑D advection equation, comparing their mathematical foundations, parameter counts, training efficiency, and prediction accuracy.

DeepONetFNOJAX
0 likes · 20 min read
Master Data‑Driven Neural Operators: DeepONet, POD‑DeepONet, FNO‑2D and Time‑Stepping FNO‑1D in One Tutorial
AI Agent Research Hub
AI Agent Research Hub
May 19, 2026 · Artificial Intelligence

Physics‑Informed Neural Networks for Navier‑Stokes Flow Parameter Identification

This tutorial demonstrates how continuous physics‑informed neural networks (PINNs) combined with stream‑function parameterization and nested forward‑mode automatic differentiation (JVP) can accurately identify the convection and viscosity coefficients of a two‑dimensional Navier‑Stokes cylinder‑wake problem from sparse velocity observations, achieving sub‑0.2% error for the convection term and robust performance even with 1% measurement noise, all within a few minutes on a single RTX 4090 GPU.

Deep LearningJAXNavier-Stokes
0 likes · 28 min read
Physics‑Informed Neural Networks for Navier‑Stokes Flow Parameter Identification
AI Agent Research Hub
AI Agent Research Hub
Apr 25, 2026 · Artificial Intelligence

AI Review Pilot at AAAI-26: 22,977 Papers Processed in 24 Hours, Accuracy Outperforms Human Reviewers

The AAAI‑26 AI Review Pilot deployed a multi‑stage GPT‑5‑based system to generate full‑text reviews for 22,977 submissions within a day at a cost of less than $1 per paper, and a large‑scale survey showed reviewers rated the AI feedback higher than human reviews on six of nine quality dimensions.

AAAI-26AISPECS Benchmark
0 likes · 25 min read
AI Review Pilot at AAAI-26: 22,977 Papers Processed in 24 Hours, Accuracy Outperforms Human Reviewers
AI Agent Research Hub
AI Agent Research Hub
Apr 22, 2026 · Artificial Intelligence

Solving the Burgers Equation with TINN: High‑Precision Physics‑Informed Neural Networks in 380 seconds

This tutorial presents the Time‑Induced Neural Network (TINN) framework that overcomes the time‑entanglement issue of standard PINNs by introducing a dedicated time‑subnet with FiLM modulation, employs a Levenberg‑Marquardt optimizer for second‑order updates, and demonstrates a 1e‑6 relative error solution of the 1‑D viscous Burgers equation in just 371 seconds on an RTX 4090.

Burgers EquationFiLM ModulationJAX
0 likes · 21 min read
Solving the Burgers Equation with TINN: High‑Precision Physics‑Informed Neural Networks in 380 seconds
AI Agent Research Hub
AI Agent Research Hub
Apr 16, 2026 · Artificial Intelligence

Conditionally Adaptive Augmented Lagrangian PINNs for Forward and Inverse PDE Solving (CMAME Open‑Source Code)

The article analyzes the multi‑objective loss imbalance in physics‑informed neural networks, introduces the CAPU algorithm that assigns independent adaptive penalty parameters via an RMSProp‑inspired update with a max‑protection rule, and demonstrates its superior accuracy on a range of forward and inverse PDE benchmarks, providing theoretical guarantees and open‑source PyTorch code.

CAPUDeep LearningPDE solving
0 likes · 23 min read
Conditionally Adaptive Augmented Lagrangian PINNs for Forward and Inverse PDE Solving (CMAME Open‑Source Code)